In wireless communication, as a modulation method of superimposing information using a carrier wave having a constant frequency and transmitting the information, a high-order modulation method, such as 16QAM (quadrature amplitude modulation) or 64QAM, has been widely used.
The 16QAM is a modulation method of changing the phase and amplitude of the carrier wave into 16 states (transmission of 16 values once) and transmitting one symbol by 4 bits. The 64QAM is a modulation method of changing the phase and amplitude of the carrier wave into 64 states (transmission of 64 values once) and transmitting one symbol by 6 bits.
Large errors occur in received data due to fading (the level of an electric wave varies due to interference between the wavelengths of electric waves having a time difference therebetween) generated in a wireless transmission line, or thermal noise inside a communication device, which results in an increase in the transmission error ratio of information.
Therefore, in the wireless communication field, an error correction encoding/decoding technique is required which allows a receiver side to correct a transmission error. Error correction encoding gives redundancy to data intended to be transmitted, and data having an error is restored by the redundancy. That is, this redundancy enables to improve transmission quality.
As the error correction encoding, so-called convolution encoding that is effected by the input of previous information data when information data is encoded has been widely used. The transmitter side performs convolution encoding on information data intended to be transmitted, modulates the encoded data, and transmits the modulated data. The receiver side demodulates received data, calculates the likelihood (probability) of the demodulated data, and estimates information data having high likelihood.
The likelihood means the conditional probability of Y being transmitted in received data X. That is, the likelihood means the probability of Y being transmitted under the conditions that data X is received.
FIG. 31 is a diagram illustrating the constellation of 16QAM. In FIG. 31, the horizontal axis indicates Ich, and the vertical axis indicates Qch. In the 16QAM transmission, the information of one of 16 symbol points is transmitted, and ideally, a signal point of a received signal is matched with any one of the 16 symbol points. However, deviation occurs in the transmission line due to, for example, noise. Here, it is assumed that the received signal is disposed at a signal point A due to deviation.
FIG. 32 is a diagram illustrating the probability of an I component of the signal point A being each signal pattern of Ich. 2-bit signal patterns of Ich are (1, 1), (1, 0), (0, 0), and (0, 1), and a component of Ich of the signal point A is any one of the signal patterns (1, 1), (1, 0), (0, 0), and (0, 1).
In the coordinates of FIG. 31, (0, 0) is closest to the signal point A, followed by (0, 1), (1, 0), and (1, 1). When the probability of a signal point being disposed at (x, y) is Px,y, the probabilities of the I component of the signal point A being disposed at (0, 0), (0, 1), (1, 0), and (1, 1) are calculated as follows: P1,1=0.05, P1,0=0.1, P0,0=0.65, and P0,1=0.2 (the probability of the I component of the signal point A being disposed at (0, 0) is the highest, and the probability of the I component of the signal point A being disposed at (1, 1) is the lowest).
Then, 2 bits of the signal pattern of Ich are separated into MSB (most significant bit) and LSB (least significant bit), and the probability of the MSB and the probability of the LSB are calculated based on the calculated probability. The calculated results are combined with each other to calculate the probability of the I component of the signal point A being each signal pattern of Ich (the left bit of the Ich signal pattern is the MSB, and the right bit thereof is the LSB).
The MSB of the I component of the signal point A is 0 when the signal pattern is (0, 0) or (0, 1). Therefore, the probability P0,L of the MSB of the I component of the signal point A being 0 is the sum of the probability P0,0 of the signal pattern being (0, 0) and the probability P0,1 of the signal pattern being (0, 1) (P0,L=P0,0+P0,1=0.65+0.2=0.85).
The MSB of the I component of the signal point A is 1 when the signal pattern is (1, 0) or (1, 1). Therefore, the probability P1,L of the MSB of the I component of the signal point A being 1 is the sum of the probability P1,0 of the signal pattern being (1, 0) and the probability P1,1 of the signal pattern being (1, 1) (P1,L=P1,0+P1,1=0.1+0.05=0.15).
The LSB of the I component of the signal point A is 0 when the signal pattern is (0, 0) or (1, 0). Therefore, the probability PM,0 of the LSB of the I component of the signal point A being 0 is the sum of the probability P0,0 of the signal pattern being (0, 0) and the probability P1,0 of the signal pattern being (1, 0) (PM,0=P0,0+P1,00=0.65+0.1=0.75).
The LSB of the I component of the signal point A is 1 when the signal pattern is (0, 1) or (1, 1). Therefore, the probability PM,1 of the LSB of the I component of the signal point A being 1 is the sum of the probability P0,1 of the signal pattern being (0, 1) and the probability P1,1 of the signal pattern being (1, 1) (PM,1=P0,1+P1,1=0.2+0.05=0.25).
When the MSB is 1 and the LSB is 1, the I component of the signal point A is the signal pattern (1, 1) of Ich. Therefore, the probability P1,1′ of the I component of the signal point A being the signal pattern (1, 1) of Ich is the product of the probability P1,L of the MSB being 1 and the probability PM,1 of the LSB being 1 (P1,1′=P1,L×PM,1=0.15×0.25=0.0375).
When the MSB is 1 and the LSB is 0, the I component of the signal point A is the signal pattern (1, 0) of Ich. Therefore, the probability P1,0′ of the I component of the signal point A being the signal pattern (1, 0) of Ich is the product of the probability P1,L of the MSB being 1 and the probability PM,0 of the LSB being 0 (P1,0′=P1,L×PM,0=0.15×0.75=0.1125).
When the MSB is 0 and the LSB is 0, the I component of the signal point A is the signal pattern (0, 0) of Ich. Therefore, the probability P0,0′ of the I component of the signal point A being the signal pattern (0, 0) of Ich is the product of the probability P0,L of the MSB being 0 and the probability PM,0 of the LSB being 0 (P0,0′=P0,L×PM,0=0.85×0.75=0.6375).
When the MSB is 0 and the LSB is 1, the I component of the signal point A is the signal pattern (0, 1) of Ich. Therefore, the probability P0,1′ of the I component of the signal point A being the signal pattern (0, 1) of Ich is the product of the probability P0,L of the MSB being 0 and the probability PM,1 of the LSB being 1 (P0,1′=P0,L×PM,1=0.85×0.25=0.2125).
FIG. 33 is a diagram illustrating the probability calculated on a bit basis and the probability calculated on a symbol basis, and illustrates the calculated probabilities as a table. As can be seen from FIG. 33, a composite probability of the probabilities calculated by separating each bit (the probability calculated on a bit basis) is different from a conventional probability (the probability calculated on a symbol basis). This means that, when the bits are separated with independent probabilities, information between the bits is lost.
In bit-based decoding, likelihood is calculated in the bit unit of the received signal. However, in error correction encoding, such as convolution encoding, information to be transmitted and a redundant bit are associated with each other. Therefore, when the receiver side calculates likelihood on a bit basis while completely separating the bits, as illustrated in FIG. 33, information between the bits is lost, which results in deterioration of characteristics (an increase in error ratio).
Therefore, the transmitter side does not perform convolution encoding on a bit basis, but performs convolution encoding on a symbol basis. The receiver side performs decoding on a symbol basis and calculates likelihood on a symbol basis. In this case, it is possible to prevent the loss of information between the bits.
A representative example of the convolution encoding technique is turbo encoding (turbo codes), which is used as a 3GPP (3rd generation partnership project) wireless communication error correction mechanism. In the turbo encoding/decoding, in order to prevent deterioration of characteristics, symbol-based encoding/decoding is performed.
As a turbo encoding technique, a technique for reducing memory usage has been proposed (for example, see Japanese Patent Application Laid-Open No. 2008-141312). In addition, a technique has been proposed which prevents deterioration of a demodulating performance without increasing interference power (for example, see Japanese Patent Application Laid-Open No. 2004-297182). Further, a technique has been proposed which improves an error correction performance using determination reliability during demodulation according to bit mapping in high-order QAM (see Japanese Patent Application Laid-Open No. 2002-344548).
In the symbol-based turbo encoding/decoding according to the related art, the transmitter side performs symbol-based turbo encoding to generate encoded data, maps the encoded data to I and Q components of the modulation method, and transmits the data. The receiver side demodulates the received data on a symbol basis, calculates the likelihood of the demodulated data on a symbol basis, and decodes the data.
As described above, in the symbol-based decoding, when a symbol is divided into bits, information between the bits is lost. Therefore, the likelihood is calculated without breaking the symbol. However, a symbol-based likelihood calculating method stores a symbol pattern. Therefore, the symbol-based likelihood calculating method has a problem in that storage capacity increases, as compared to a bit-based likelihood calculating method.
In addition, as the number of multiple values in the modulate method is increased, the number of patterns to be stored is exponentially increased. Therefore, the symbol-based turbo encoding/decoding causes an increase in circuit size.
Meanwhile, when the symbol-based turbo encoding is performed, symbol-based interleaving is performed. When the bit-based turbo encoding is performed, bit-based interleaving is performed. However, the symbol-based turbo encoding has a problem in that capability to arrange data at random (random property) is lowered, as compared to the bit-based turbo encoding. As a result, in the symbol-based turbo encoding, characteristics deteriorate.